import libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import sklearn

from plotly.offline import init_notebook_mode
init_notebook_mode(connected=False)

df = pd.read_csv('./Data/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df.head()

	Age	Attrition	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Yes	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	...	1	80	0	8	0	1	6	4	0	5
1	49	No	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	...	4	80	1	10	3	3	10	7	1	7
2	37	Yes	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	...	2	80	0	7	3	3	0	0	0	0
3	33	No	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	...	3	80	0	8	3	3	8	7	3	0
4	27	No	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	...	4	80	1	6	3	3	2	2	2	2

5 rows × 35 columns

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 35 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Age                       1470 non-null   int64 
 1   Attrition                 1470 non-null   object
 2   BusinessTravel            1470 non-null   object
 3   DailyRate                 1470 non-null   int64 
 4   Department                1470 non-null   object
 5   DistanceFromHome          1470 non-null   int64 
 6   Education                 1470 non-null   int64 
 7   EducationField            1470 non-null   object
 8   EmployeeCount             1470 non-null   int64 
 9   EmployeeNumber            1470 non-null   int64 
 10  EnvironmentSatisfaction   1470 non-null   int64 
 11  Gender                    1470 non-null   object
 12  HourlyRate                1470 non-null   int64 
 13  JobInvolvement            1470 non-null   int64 
 14  JobLevel                  1470 non-null   int64 
 15  JobRole                   1470 non-null   object
 16  JobSatisfaction           1470 non-null   int64 
 17  MaritalStatus             1470 non-null   object
 18  MonthlyIncome             1470 non-null   int64 
 19  MonthlyRate               1470 non-null   int64 
 20  NumCompaniesWorked        1470 non-null   int64 
 21  Over18                    1470 non-null   object
 22  OverTime                  1470 non-null   object
 23  PercentSalaryHike         1470 non-null   int64 
 24  PerformanceRating         1470 non-null   int64 
 25  RelationshipSatisfaction  1470 non-null   int64 
 26  StandardHours             1470 non-null   int64 
 27  StockOptionLevel          1470 non-null   int64 
 28  TotalWorkingYears         1470 non-null   int64 
 29  TrainingTimesLastYear     1470 non-null   int64 
 30  WorkLifeBalance           1470 non-null   int64 
 31  YearsAtCompany            1470 non-null   int64 
 32  YearsInCurrentRole        1470 non-null   int64 
 33  YearsSinceLastPromotion   1470 non-null   int64 
 34  YearsWithCurrManager      1470 non-null   int64 
dtypes: int64(26), object(9)
memory usage: 402.1+ KB

df.describe()

	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
count	1470.000000	1470.000000	1470.000000	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	...	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000
mean	36.923810	802.485714	9.192517	2.912925	1.0	1024.865306	2.721769	65.891156	2.729932	2.063946	...	2.712245	80.0	0.793878	11.279592	2.799320	2.761224	7.008163	4.229252	2.187755	4.123129
std	9.135373	403.509100	8.106864	1.024165	0.0	602.024335	1.093082	20.329428	0.711561	1.106940	...	1.081209	0.0	0.852077	7.780782	1.289271	0.706476	6.126525	3.623137	3.222430	3.568136
min	18.000000	102.000000	1.000000	1.000000	1.0	1.000000	1.000000	30.000000	1.000000	1.000000	...	1.000000	80.0	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000
25%	30.000000	465.000000	2.000000	2.000000	1.0	491.250000	2.000000	48.000000	2.000000	1.000000	...	2.000000	80.0	0.000000	6.000000	2.000000	2.000000	3.000000	2.000000	0.000000	2.000000
50%	36.000000	802.000000	7.000000	3.000000	1.0	1020.500000	3.000000	66.000000	3.000000	2.000000	...	3.000000	80.0	1.000000	10.000000	3.000000	3.000000	5.000000	3.000000	1.000000	3.000000
75%	43.000000	1157.000000	14.000000	4.000000	1.0	1555.750000	4.000000	83.750000	3.000000	3.000000	...	4.000000	80.0	1.000000	15.000000	3.000000	3.000000	9.000000	7.000000	3.000000	7.000000
max	60.000000	1499.000000	29.000000	5.000000	1.0	2068.000000	4.000000	100.000000	4.000000	5.000000	...	4.000000	80.0	3.000000	40.000000	6.000000	4.000000	40.000000	18.000000	15.000000	17.000000

8 rows × 26 columns

df.columns
print(df.columns)

Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',
       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',
       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',
       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',
       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',
       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',
       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',
       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',
       'YearsWithCurrManager'],
      dtype='object')

employee attrition value

df['Attrition'].value_counts().plot(kind='bar', backend='plotly', title='Attrition')

employee attrition %

attrition_counts = df["Attrition"].value_counts()
yes_percent = attrition_counts["Yes"] / (
    attrition_counts["Yes"] + attrition_counts["No"]
)
no_percent = attrition_counts["No"] / (attrition_counts["Yes"] + attrition_counts["No"])

fig = go.Figure(
    data=[
        go.Pie(
            labels=["Yes", "No"],
            values=[yes_percent, no_percent],
        ),
    ]
)
fig.show()

gender analysis

# average age of genders in the dataset
average_age_by_gender = df.groupby('Gender')['Age'].mean()
print(average_age_by_gender)

Gender
Female    37.329932
Male      36.653061
Name: Age, dtype: float64

age distribution

from plotly.subplots import make_subplots

# Create subplots: 2 rows, 2 columns
fig = make_subplots(rows=2, cols=2, subplot_titles=("Female Employees", "Male Employees", "Overall Employees"))

# Female employees
female_df = df[df['Gender'] == 'Female']
female_hist = px.histogram(female_df, x='Age')
female_hist.update_traces(showlegend=True)
mean_age_female = female_df['Age'].mean()

# Male employees
male_df = df[df['Gender'] == 'Male']
male_hist = px.histogram(male_df, x='Age')
male_hist.update_traces(showlegend=True)
mean_age_male = male_df['Age'].mean()

# Overall employees
overall_hist = px.histogram(df, x='Age')
overall_hist.update_traces(showlegend=True)
mean_age_overall = df['Age'].mean()

# Add traces to subplots
for trace in female_hist.data:
    fig.add_trace(trace, row=1, col=1)
    fig.update_xaxes(title_text="Age", row=1, col=1)
    fig.update_yaxes(title_text="Count", row=1, col=1)
    fig.add_vline(x=mean_age_female, line_width=2, line_dash="dash", line_color="red", annotation_text="Mean Age", annotation_position="top right", row=1, col=1)

for trace in male_hist.data:
    fig.add_trace(trace, row=1, col=2)
    fig.update_xaxes(title_text="Age", row=1, col=2)
    fig.update_yaxes(title_text="Count", row=1, col=2)
    fig.add_vline(x=mean_age_male, line_width=2, line_dash="dash", line_color="red", annotation_text="Mean Age", annotation_position="top right", row=1, col=2)

for trace in overall_hist.data:
    fig.add_trace(trace, row=2, col=1)
    fig.update_xaxes(title_text="Age", row=2, col=1)
    fig.update_yaxes(title_text="Count", row=2, col=1)
    fig.add_vline(x=mean_age_overall, line_width=2, line_dash="dash", line_color="red", annotation_text="Mean Age", annotation_position="top right", row=2, col=1)

# Update layout
fig.update_layout(title_text="Age Distribution of Employees", height=800)

fig.show()

distribution of job satisfaction

df.groupby(['Gender', 'JobSatisfaction'])['JobSatisfaction'].count()

Gender  JobSatisfaction
Female  1                  119
        2                  118
        3                  181
        4                  170
Male    1                  170
        2                  162
        3                  261
        4                  289
Name: JobSatisfaction, dtype: int64

# Group by Gender and JobSatisfaction and count the occurrences
grouped_df = (
    df.groupby(["Gender", "JobSatisfaction"])["JobSatisfaction"]
    .count()
    .reset_index(name="Count")
)

# Create a bar plot
fig = px.bar(
    grouped_df,
    x="JobSatisfaction",
    y="Count",
    color="Gender",
    barmode="group",
    title="Job Satisfaction by Gender",
)

fig.show()

Monthly Income by Gender

fig = px.strip(
    df,
    x='Gender',
    y='MonthlyIncome',
    title='Disparity in Monthly Income',
    hover_data=['MonthlyIncome', 'JobSatisfaction'],
    color='Gender'
)
fig.show()

Average Income and Presence by Department

# Calculate the mean monthly income by gender
mean_income = df.groupby('Gender')['MonthlyIncome'].mean().reset_index()

# Create a bar plot
fig = px.bar(mean_income, x='Gender', y='MonthlyIncome', title='Average Monthly Income by Gender', color='Gender')
fig.show()

grouped_df = df.groupby(['Gender', 'Department'])['Department'].count().reset_index(name='Count')

# Create a bar plot
fig = px.bar(
    grouped_df,
    x="Department",
    y="Count",
    color="Gender",
    barmode="group",
    title="Number of Employees by Department",
)

fig.show()